Evaluation is persuasive only when the measured outcome corresponds to the construct claimed by the study and the comparison answers the stated research question. This structured evidence review evaluates "Hierarchical Spatial Mamba Framework for Point Cloud Classification" alongside nine author-disjoint, topically matched publications in three-dimensional perception. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through evaluation design and construct validity, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda aligns claims, outcomes, comparators, sampling, and uncertainty before any performance estimate is interpreted.
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- Usami, H., Saito, H., Kawai, J., & Itani, N. (2018). Synchronizing 3D point cloud from 3D scene flow estimation with 3D Lidar and RGB camera. Electronic Imaging, 30(18), 426-1-426-6. https://doi.org/10.2352/issn.2470-1173.2018.18.3dipm-426 DOI
- Guo, Y., & Hu, H. (2024). Multi-Layer Fusion 3D Object Detection via Lidar Point Cloud and Camera Image. Applied Sciences, 14(4), 1348. https://doi.org/10.3390/app14041348 DOI
- Wang, Z., Zhan, W., & Tomizuka, M. (2018). Fusing Bird’s Eye View LIDAR Point Cloud and Front View Camera Image for 3D Object Detection. 2018 IEEE Intelligent Vehicles Symposium (IV), 1-6. https://doi.org/10.1109/ivs.2018.8500387 DOI
- Abdelgawad, Y. (2025). Unveiling the Depths: A Comprehensive Comparison of Monocular Depth Models and LiDAR-Camera 3D Perception. . https://doi.org/10.58445/rars.2821 DOI
- Sadakuni, Y., Kusakari, R., Onda, K., & Kuroda, Y. (2019). Construction of Highly Accurate Depth Estimation Dataset Using High Density 3D LiDAR and Stereo Camera. 2019 IEEE/SICE International Symposium on System Integration (SII), 554-559. https://doi.org/10.1109/sii.2019.8700333 DOI
- Du, Y., & Zheng, G. (2024). 3D Target Detection Based on Image and Lidar Point Cloud Fusion Under Depth Complementation. 2024 IEEE 4th International Conference on Software Engineering and Artificial Intelligence (SEAI), 117-122. https://doi.org/10.1109/seai62072.2024.10674495 DOI
- Pal, B., Khaiyum, S., & Kumaraswamy, Y.-S. (2017). 3D point cloud generation from 2D depth camera images using successive triangulation. 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 129-133. https://doi.org/10.1109/icimia.2017.7975586 DOI
- Liu, J., Yue, S., Hao, W., & Cai, Y. (2026). MF-BEVFusion: multiscale depth estimation and fully dynamic fusion for camera-LiDAR BEV 3D object detection. Journal of Electronic Imaging, 35(02). https://doi.org/10.1117/1.jei.35.2.023009 DOI
- Konno, J., & Ando, Y. (2022). Improvement of 3D-SLAM Accuracy by Removing Moving Objects on 3D-LiDAR Point Cloud Using Image Recognition in Web Camera. 2022 22nd International Conference on Control, Automation and Systems (ICCAS), 527-531. https://doi.org/10.23919/iccas55662.2022.10003848 DOI
- Journal
- Convergence in Science and Society
- Volume
- 1 (2026)
- Article number
- css20260066
- License
- CC BY 4.0